2020
DOI: 10.1101/2020.04.14.040329
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Efficient phasing and imputation of low-coverage sequencing data using large reference panels

Abstract: 10Low-coverage whole genome sequencing followed by imputation has been proposed as a 11 cost-effective genotyping approach for disease and population genetics studies. However, its 12 competitiveness against SNP arrays is undermined as current imputation methods are 13 computationally expensive and unable to leverage large reference panels. 14 Here, we describe a method, GLIMPSE, for phasing and imputation of low-coverage 15 sequencing datasets from modern reference panels. We demonstrate its remarkable 16 per… Show more

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Cited by 23 publications
(42 citation statements)
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“…Previous aDNA studies have used Beagle 4.0 to impute ancient genomes 10 13 , which accepts genotype likelihood input to estimate genotypes at all sites in the reference panel in a single step. Another imputation tool recently developed for low-coverage sequencing data, GLIMPSE, functions in a similar way 9 . We compared the performance of these one-step procedures to our two-step pipeline (Fig.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Previous aDNA studies have used Beagle 4.0 to impute ancient genomes 10 13 , which accepts genotype likelihood input to estimate genotypes at all sites in the reference panel in a single step. Another imputation tool recently developed for low-coverage sequencing data, GLIMPSE, functions in a similar way 9 . We compared the performance of these one-step procedures to our two-step pipeline (Fig.…”
Section: Resultsmentioning
confidence: 99%
“…One solution is to use a probabilistic measurement of the genotypes in the form of genotype probabilities or genotype likelihoods. Imputation tools that accept probabilistic genotype input include Beagle (≤ 4.1) 7 , IMPUTE 2 8 , and GLIMPSE 9 . Up until Beagle 4.0, the algorithm can produce genotypes for all sites in the reference panel in one step similar to IMPUTE 2 and GLIMPSE; in Beagle 4.1, the genotype likelihood mode only updates sites in the input file, requiring another step in the genotype mode to impute the missing genotypes.…”
Section: Introductionmentioning
confidence: 99%
“…ancients and moderns) (Wohns et al 2021). A possibility for making lower coverage ancient genomes, or indeed hybrid capture array data, accessible to these methods is imputation (Rubinacci et al 2020; Hui et al 2020). A potential concern is that imputation may introduce biases, particularly in ancient genomes with ancestries that are not well reflected in modern groups.…”
Section: Discussionmentioning
confidence: 99%
“…As genome sequencing costs have decreased over the past decade, sequencing-based alternatives to genotyping arrays have been the subject of growing interest (Wetterstrand, 2019). Specifically, low-coverage shotgun whole genome sequencing followed by imputation has been utilized for a number of problems in statistical and population genetics, from providing the backbone for graphbased pangenomes in sorghum to trait mapping in human pharmacogenetics (Tran et al, 2020;Gilly et al, 2019;Rubinacci et al, 2020;Homburger et al, 2019;Wasik et al, 2019;Jensen et al, 2020;Cai et al, 2015a;Liu et al, 2018). As an intuition for why this approach is useful, a sample sequenced at a target coverage of 0.5× is expected to have at least one read on 33 million of the 85 million sites in the 1000 Genomes Phase 3 release, whereas a genotyping array will probe a number of variants which is one to two orders of magnitude fewer, albeit with higher average accuracy (Consortium et al, 2015).…”
Section: Introductionmentioning
confidence: 99%